Primary immunodeficiency diseases of adults: a review of pulmonary complication imaging findings.

Our objective in this review is to familiarize radiologists with the spectrum of initial and progressive CT manifestations of pulmonary complications observed in adult patients with primary immunodeficiency diseases, including primary antibody deficiency (PAD), hyper-IgE syndrome (HIES), and chronic granulomatous disease (CGD). In patients with PAD, recurrent pulmonary infections may lead to airway remodeling with bronchial wall-thickening, bronchiectasis, mucus-plugging, mosaic perfusion, and expiratory air-trapping. Interstitial lung disease associates pulmonary lymphoid hyperplasia, granulomatous inflammation, and organizing pneumonia and is called granulomatous-lymphocytic interstitial lung disease (GLILD). The CT features of GLILD are solid and semi-solid pulmonary nodules and areas of air space consolidation, reticular opacities, and lymphadenopathy. These features may overlap those of mucosa-associated lymphoid tissue (MALT) lymphoma, justifying biopsies. In patients with HIES, particularly the autosomal dominant type (Job syndrome), recurrent pyogenic infections lead to permanent lung damage. Secondary infections with aspergillus species develop in pre-existing pneumatocele and bronchiectasis areas, leading to chronic airway infection. The complete spectrum of CT pulmonary aspergillosis may be seen including aspergillomas, chronic cavitary pulmonary aspergillosis, allergic bronchopulmonary aspergillosis (ABPA)-like pattern, mixed pattern, and invasive. Patients with CGD present with recurrent bacterial and fungal infections leading to parenchymal scarring, traction bronchiectasis, cicatricial emphysema, airway remodeling, and mosaicism. Invasive aspergillosis, the major cause of mortality, manifests as single or multiple nodules, areas of airspace consolidation that may be complicated by abscess, empyema, or contiguous extension to the pleura or chest wall. CLINICAL RELEVANCE STATEMENT: Awareness of the imaging findings spectrum of pulmonary complications that can occur in adult patients with primary immunodeficiency diseases is important to minimize diagnostic delay and improve patient outcomes. KEY POINTS: • Unexplained bronchiectasis, associated or not with CT findings of obliterative bronchiolitis, should evoke a potential diagnosis of primary autoantibody deficiency. • The CT evidence of various patterns of aspergillosis developed in severe bronchiectasis or pneumatocele in a young adult characterizes the pulmonary complications of hyper-IgE syndrome. • In patients with chronic granulomatous disease, invasive aspergillosis is relatively frequent, often asymptomatic, and sometimes mimicking or associated with non-infectious inflammatory pulmonary lesions.

Idiopathic inflammatory myopathies: CT characteristics of interstitial lung disease and their association(s) with myositis-specific autoantibodies.

OBJECTIVES: Interstitial lung disease (ILD), one of the most common extramuscular manifestations of idiopathic inflammatory myopathies (IIMs), carries a poor prognosis. Myositis-specific autoantibody (MSA)-positivity is a key finding for IIM diagnosis. We aimed to identify IIM-associated lung patterns, evaluate potential CT-ILD finding-MSA relationships, and assess intra- and interobserver reproducibility in a large IIM population. METHODS: All consecutive IIM patients (2003-2019) were included. Two chest radiologists retrospectively assessed all chest CT scans. Multiple correspondence and hierarchical cluster analyses of CT findings identified and characterized ILD-patient subgroups. Classification and regression-tree analyses highlighted CT-scan variables predicting three patterns. Three independent radiologists read CT scans twice to assign patients according to CT-ILD-pattern clusters. RESULTS: Among 257 IIM patients, 94 (36.6%) had ILDs; 87 (93%) of them were MSA-positive. ILD-IIM distribution was 54 (57%) ASyS, 21 (22%) DM, 15 (16%) IMNM, and 4 (4%) IBM. Cluster analysis identified three ILD-patient subgroups. Consolidation characterized cluster 1, with significantly (p < 0.05) more frequent anti-MDA5-autoantibody-positivity. Significantly more cluster-2 patients had a reticular pattern, without cysts and with few consolidations. All cluster-3 patients had cysts and anti-PL12 autoantibodies. Clusters 2 and 3 included significantly more ASyS patients. Intraobserver concordances to classify patients into those three clusters were good-to-excellent (Cohen κ 0.64-0.81), with good interobserver reliability (Fleiss's κ 0.56). CONCLUSION: Despite the observed IIM heterogeneity, CT-scan criteria enabled ILD assignment to the three clusters, which were associated with MSAs. Radiologist identification of those clusters could facilitate diagnostic screening and therapeutics. Interstitial lung disease in patients with idiopathic inflammatory myopathy could be classified into three clusters according to CT-scan criteria, and these clusters were significantly associated with myositis-specific autoantibodies. KEY POINTS: • Cluster analysis discerned three homogeneous groups of interstitial lung disease (ILD) for which cysts, consolidations, and reticular pattern were discriminatory, and associated with myositis-specific autoantibodies. • Like muscle- and extramuscular-specific phenotypes, myositis-specific autoantibodies are also associated with specific ILD patterns in patients with idiopathic inflammatory myopathies.

Isolating Nanoparticles from Complex Biological Media by Immunoprecipitation.

Polyethylene glycol (PEG) is considered the gold standard to prepare long circulating nanoparticles. The hydrophilic layer that sterically protects PEGylated nanomedicines also impedes their separation from biological media. In this study, we describe an immunoprecipitation method using AntiPEG antibodies cross-linked to magnetic beads to extract three types of radiolabeled PEGylated systems: polymeric nanoparticles, liposomes, and therapeutic proteins. The potential of the method is emphasized by isolating these systems after in vivo administration and ex vivo incubation in human biological fluids. Immunoprecipitation also allows a unique perspective on the size distribution of nanoparticles in the bloodstream after intravenous and intraperitoneal administrations. Further, we highlight the potential of the approach to inform on nanomaterial-associated drug in plasma as well as help characterize the protein corona. Altogether, we believe this method answers an unmet need in nanomedicine research and will contribute a fresh perspective on the interactions of nanomedicines with biological systems.

COVID-19 pneumonia: high diagnostic accuracy of chest CT in patients with intermediate clinical probability.

OBJECTIVES: To assess inter-reader agreements and diagnostic accuracy of chest CT to identify COVID-19 pneumonia in patients with intermediate clinical probability during an acute disease outbreak. METHODS: From March 20 to April 8, 319 patients (mean age 62.3 years old) consecutive patients with an intermediate clinical probability of COVID-19 pneumonia underwent a chest CT scan. Two independent chest radiologists blinded to clinical information and RT-PCR results retrospectively reviewed and classified images on a 1-5 confidence level scale for COVID-19 pneumonia. Agreements between radiologists were assessed with kappa statistics. Diagnostic accuracy of chest CT compared with RT-PCR assay and patient outcomes was measured using receiver operating characteristics (ROC). Positive predictive value (PPV) and negative predictive value (NPV) for COVID-19 pneumonia were calculated. RESULTS: Inter-observer agreement for highly probable (kappa: 0.83 [p < .001]) and highly probable or probable (kappa: 0.82 [p < .001]) diagnosis of COVID-19 pneumonia was very good. RT-PCR tests performed in 307 patients were positive in 174 and negative in 133. The areas under the curve (AUC) were 0.94 and 0.92 respectively. With a disease prevalence of 61.2%, PPV were 95.9% and 94.3%, and NPV 84.4% and 77.1%. CONCLUSION: During acute COVID-19 outbreak, chest CT scan may be used for triage of patients with intermediate clinical probability with very good inter-observer agreements and diagnostic accuracy. KEY POINTS: • Concordances between two chest radiologists to diagnose or exclude a COVID-19 pneumonia in 319 consecutive patients with intermediate clinical probability were very good (kappa: 0.82; p < .001). • When compared with RT-PCR results and patient outcomes, the diagnostic accuracy of CT to identify COVID-19 pneumonia was high for both radiologists (AUC: 0.94 and 0.92). • With a disease prevalence of 61.2% in the studied population, the positive predictive values of CT for diagnosing COVID-19 pneumonia were 95.9% and 94.3% with negative predictive values of 84.4% and 77.1%.

Vascular Ehlers-Danlos syndrome (vEDS): CT and histologic findings of pleural and lung parenchymal damage.

OBJECTIVES: To describe CT features of lung involvement in patients with vascular Ehlers-Danlos syndrome (vEDS), a rare genetic condition caused by pathogenic variants within the COL3A1 gene, characterized by recurrent arterial, digestive, and pulmonary events. MATERIAL AND METHODS: All consecutive vEDS patients referred to the national tertiary referral center for vEDS, between 2004 and 2016, were included. Chest CT scans obtained during the initial vascular work-up were reviewed retrospectively by two chest radiologists for lung involvement. Five surgical samples underwent histologic examination. RESULTS: Among 136 enrolled patients (83 women, 53 men; mean age 37 years) with molecularly confirmed vEDS, 24 (17.6%) had a history of respiratory events: 17 with pneumothorax, 4 with hemothorax, and 3 with hemoptysis that required thoracic surgery in 11. CT scans detected lung parenchymal abnormalities in 78 (57.3%) patients: emphysema (mostly centrilobular and paraseptal) in 44 (32.3%), comparable for smokers and non-smokers; clusters of calcified small pulmonary nodules in 9 (6.6%); and cavitated nodules in 4 (2.9%). Histologic examination of surgical samples found arterial abnormalities, emphysema with alveolar ruptures in 3, accompanied by diffuse hemorrhage and increased hemosiderin resorption. CONCLUSION: In vEDS patients, identification of lung parenchymal abnormalities is common on CT. The most frequently observed CT finding was emphysema suggesting alveolar wall rupture which might facilitate the diagnostic screening of the disease in asymptomatic carriers of a genetic COL3A1 gene mutation. The prognostic value and evolution of these parenchymal abnormalities remain to be evaluated. KEY POINTS: • Patients with vEDS can have lung parenchymal changes on top of or next to thoracal vascular abnormalities and that these changes can be present in asymptomatic cases. • The presence of these parenchymal changes is associated with a slightly higher incidence of respiratory events (although not statistically significant). • Identification of the described CT pattern by radiologists and chest physicians may facilitate diagnostic screening.

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort.

OBJECTIVES: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs. METHODS: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs. A metric-based approach for the classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning-based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. RESULTS: Most discriminative features of COVID-19 are the percentage of airspace opacity and peripheral and basal predominant opacities, concordant with the typical characterization of COVID-19 in the literature. Unsupervised hierarchical clustering compares feature distribution across COVID-19 and control cohorts. The metrics-based classifier achieved AUC = 0.83, sensitivity = 0.74, and specificity = 0.79 versus respectively 0.93, 0.90, and 0.83 for the DL-based classifier. Most of ambiguity comes from non-COVID-19 pneumonia with manifestations that overlap with COVID-19, as well as mild COVID-19 cases. Non-COVID-19 classification performance is 91% for ILD, 64% for other pneumonias, and 94% for no pathologies, which demonstrates the robustness of our method against different compositions of control groups. CONCLUSIONS: Our new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and CTs with no pathologies, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance and, therefore, may be useful to facilitate diagnosis of COVID-19. KEY POINTS: • Unsupervised clustering reveals the key tomographic features including percent airspace opacity and peripheral and basal opacities most typical of COVID-19 relative to control groups. • COVID-19-positive CTs were compared with COVID-19-negative chest CTs (including a balanced distribution of non-COVID-19 pneumonia, ILD, and no pathologies). Classification accuracies for COVID-19, pneumonia, ILD, and CT scans with no pathologies are respectively 90%, 64%, 91%, and 94%. • Our deep learning (DL)-based classification method demonstrates an AUC of 0.93 (sensitivity 90%, specificity 83%). Machine learning methods applied to quantitative chest CT metrics can therefore improve diagnostic accuracy in suspected COVID-19, particularly in resource-constrained environments.

Characterization of Interstitial Lung Disease Associated With Anti-Ribonucleoprotein Antibodies.

OBJECTIVES: Interstitial lung disease (ILD) is a common feature of mixed connective tissue disease. However, many patients do not meet the criteria for mixed connective tissue disease and thus may be diagnosed as interstitial pneumonia with autoimmune features. The aim of this study was to characterize ILD associated with anti-ribonucleoprotein (RNP) antibodies. METHODS: Chest computed tomography scans of patients with anti-RNP antibody who were seen between January 2011 and October 2015 were reviewed. The underlying disease was classified with international criteria using clinical and serological features. RESULTS: Among 544 patients with anti-RNP antibodies, 188 had a chest computed tomography scan, and 48 (26%) of them had radiological features of ILD. The presence of ILD was significantly associated with dyspnea, crackles, arthritis, Raynaud phenomenon, myositis, and sicca syndrome. The most frequent pattern was nonspecific interstitial pneumonia in 39 patients (81%). Among patients with ILD, 17 (35%) had a radiological pattern consisting of cysts and ground-glass attenuation not fulfilling the lymphoid interstitial pneumonia criteria. In 3 patients, cysts were related to fibrosis; in 14 patients, cysts corresponded to an original ILD pattern. CONCLUSIONS: Interstitial lung disease was found in 26% of patients with anti-RNP antibodies independently of the underlying disease. Anti-RNP-associated ILD mainly corresponds to nonspecific interstitial pneumonia or an original pattern consisting of cysts and ground-glass attenuation.

Relationship of spirometric, body plethysmographic, and diffusing capacity parameters to emphysema scores derived from CT scans.

Phenotyping of chronic obstructive pulmonary disease (COPD) with computed tomography (CT) is used to distinguish between emphysema- and airway-dominated type. The phenotype is reflected in correlations with lung function measures. Among these, the relative value of body plethysmography has not been quantified. We addressed this question using CT scans retrospectively collected from clinical routine in a large COPD cohort. Three hundred and thirty five patients with baseline data of the German COPD cohort COPD and Systemic Consequences-Comorbidities Network were included. CT scans were primarily evaluated using a qualitative binary emphysema score. The binary score was positive for emphysema in 52.5% of patients, and there were significant differences between the positive/negative groups regarding forced expiratory volume in 1 second (FEV1), FEV1/forced vital capacity (FVC), intrathoracic gas volume (ITGV), residual volume (RV), specific airway resistance (sRaw), transfer coefficient (KCO), transfer factor for carbon monoxide (TLCO), age, pack-years, and body mass index (BMI). Stepwise discriminant analyses revealed the combination of FEV1/FVC, RV, sRaw, and KCO to be significantly related to the binary emphysema score. The additional positive predictive value of body plethysmography, however, was only slightly higher than that of the conventional combination of spirometry and diffusing capacity, which if taken alone also achieved high predictive values, in contrast to body plethysmography. The additional information on the presence of CT-diagnosed emphysema as conferred by body plethysmography appeared to be minor compared to the well-known combination of spirometry and CO diffusing capacity.

Subsolid Lung Nodule Classification: A CT Criterion for Improving Interobserver Agreement.

Purpose To evaluate an objective computed tomographic (CT) criterion for distinguishing between part-solid (PS) and nonsolid (NS) lung nodules. Materials and Methods This study received institutional review board approval, and patients gave informed consent. Preoperative CT studies in all patients who underwent surgery for subsolid nodules between 2008 and 2015 were first reviewed by two senior radiologists, who subjectively classified the nodules as PS or NS. A second reading performed 1 month later used predefined classification criteria and involved a third senior radiologist as well as three junior radiologists. Subsolid nodules were classified as PS if a solid portion was detectable in the mediastinal window setting (nonmeasurable, < 50%, or > 50% of the entire nodule) and were otherwise classified as NS (subclassified as pure or heterogeneous). Interreader agreement was assessed with κ statistics and the intraclass correlation coefficient (ICC). Results A total of 99 nodules measuring a median of 20 mm (range, 5-47 mm) in lung window CT images were analyzed. Senior radiologist agreement on the PS/NS distinction increased from moderate (κ = 0.54; 95% confidence interval [CI]: 0.37, 0.71) to excellent (κ = 0.89; 95% CI: 0.80, 0.98) between the first and second readings. At the second readings, agreement among senior and junior radiologists was excellent for PS/NS distinction (ICC = 0.87; 95% CI: 0.83, 0.90) and for subcategorization (ICC = 0.82; 95% CI: 0.77, 0.87). When a solid portion was measurable in the mediastinal window, the specificity for adenocarcinoma invasiveness ranged from 86% to 96%. Conclusion Detection of a solid portion in the mediastinal window setting allows subsolid nodules to be classified as PS with excellent interreader agreement. If the solid portion is measurable, the specificity for adenocarcinoma invasiveness is high. © RSNA, 2017 Online supplemental material is available for this article.

CT-based Visual Classification of Emphysema: Association with Mortality in the COPDGene Study.

Purpose To determine whether visually assessed patterns of emphysema at CT might provide a simple assessment of mortality risk among cigarette smokers. Materials and Methods Of the first 4000 cigarette smokers consecutively enrolled between 2007 and 2011 in this COPDGene study, 3171 had data available for both visual emphysema CT scores and survival. Each CT scan was retrospectively visually scored by two analysts using the Fleischner Society classification system. Severity of emphysema was also evaluated quantitatively by using percentage lung volume occupied by low-attenuation areas (voxels with attenuation of -950 HU or less) (LAA-950). Median duration of follow-up was 7.4 years. Regression analysis for the relationship between imaging patterns and survival was based on the Cox proportional hazards model, with adjustment for age, race, sex, height, weight, pack-years of cigarette smoking, current smoking status, educational level, LAA-950, and (in a second model) forced expiratory volume in 1 second (FEV1). Results Observer agreement in visual scoring was good (weighted κ values, 0.71-0.80). There were 519 deaths in the study cohort. Compared with subjects who did not have visible emphysema, mortality was greater in those with any grade of emphysema beyond trace (adjusted hazard ratios, 1.7, 2.5, 5.0, and 4.1, respectively, for mild centrilobular emphysema, moderate centrilobular emphysema, confluent emphysema, and advanced destructive emphysema, P < .001). This increased mortality generally persisted after adjusting for LAA-950. Conclusion The visual presence and severity of emphysema is associated with significantly increased mortality risk, independent of the quantitative severity of emphysema. Online supplemental material is available for this article.

CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society.

The purpose of this statement is to describe and define the phenotypic abnormalities that can be identified on visual and quantitative evaluation of computed tomographic (CT) images in subjects with chronic obstructive pulmonary disease (COPD), with the goal of contributing to a personalized approach to the treatment of patients with COPD. Quantitative CT is useful for identifying and sequentially evaluating the extent of emphysematous lung destruction, changes in airway walls, and expiratory air trapping. However, visual assessment of CT scans remains important to describe patterns of altered lung structure in COPD. The classification system proposed and illustrated in this article provides a structured approach to visual and quantitative assessment of COPD. Emphysema is classified as centrilobular (subclassified as trace, mild, moderate, confluent, and advanced destructive emphysema), panlobular, and paraseptal (subclassified as mild or substantial). Additional important visual features include airway wall thickening, inflammatory small airways disease, tracheal abnormalities, interstitial lung abnormalities, pulmonary arterial enlargement, and bronchiectasis.

Kidney stones and imaging: what can your radiologist do for you?

OBJECTIVES: We discuss in this review, urologists' expectations of imaging in terms of detection, characterization, pre-planning treatment and follow-up of urinary stones. MATERIALS AND METHODS: Data acquisition regarding kidney stones and imaging was performed using MEDLINE searches with combinations of the following keywords: urinary stones, CT Urography, low dose CT, MRI urography, renal stones ultrasound, conventional radiography, surgery. RESULTS: CT has become the gold standard for the evaluation of urinary stones. Scanning provides information regarding stone (composition, size, burden, location), collecting system and renal parenchyma. Those findings are crucial in determining appropriate treatment strategies. Because CT exposes the patient to substantial ionizing radiation, efforts have already been made to decrease the CT radiation dose for CT examination (low dose CT) and optimize image quality. Efforts also are being made to use non ionizing modalities such as ultrasound in combination with radiography particularly for the follow up of renal stones. CONCLUSION: CT is the preferred method for the evaluation and treatment planning of urolithiasis. CT radiation dose reduction can be achieved with low dose CT. However, conventional radiography and ultrasound are still recommended in the follow up of renal stones.

A deep learning-based algorithm improves radiology residents' diagnoses of acute pulmonary embolism on CT pulmonary angiograms.

PURPOSE: To compare radiology residents' diagnostic performances to detect pulmonary emboli (PEs) on CT pulmonary angiographies (CTPAs) with deep-learning (DL)-based algorithm support and without. METHODS: Fully anonymized CTPAs (n = 207) of patients suspected of having acute PE served as input for PE detection using a previously trained and validated DL-based algorithm. Three residents in their first three years of training, blinded to the index report and clinical history, read the CTPAs first without, and 2 months later with the help of artificial intelligence (AI) output, to diagnose PE as present, absent or indeterminate. We evaluated concordances and discordances with the consensus-reading results of two experts in chest imaging. RESULTS: Because the AI algorithm failed to analyze 11 CTPAs, 196 CTPAs were analyzed; 31 (15.8 %) were PE-positive. Good-classification performance was higher for residents with AI-algorithm support than without (AUROCs: 0.958 [95 % CI: 0.921-0.979] vs. 0.894 [95 % CI: 0.850-0.931], p < 0.001, respectively). The main finding was the increased sensitivity of residents' diagnoses using the AI algorithm (92.5 % vs. 81.7 %, respectively). Concordance between residents (kappa: 0.77 [95 % CI: 0.76-0.78]; p < 0.001) improved with AI-algorithm use (kappa: 0.88 [95 % CI: 0.87-0.89]; p < 0.001). CONCLUSION: The AI algorithm we used improved between-resident agreements to interpret CTPAs for suspected PE and, hence, their diagnostic performances.

Myelin-reactive B cells exacerbate CD4+ T cell-driven CNS autoimmunity in an IL-23-dependent manner.

B cells and T cells collaborate in multiple sclerosis (MS) pathogenesis. IgH[MOG] mice possess a B cell repertoire skewed to recognize myelin oligodendrocyte glycoprotein (MOG). Here, we show that upon immunization with the T cell-obligate autoantigen, MOG[35-55], IgH[MOG] mice develop rapid and exacerbated experimental autoimmune encephalomyelitis (EAE) relative to wildtype (WT) counterparts, characterized by aggregation of T and B cells in the IgH[MOG] meninges and by CD4+ T helper 17 (Th17) cells in the CNS. Production of the Th17 maintenance factor IL-23 is observed from IgH[MOG] CNS-infiltrating and meningeal B cells, and in vivo blockade of IL-23p19 attenuates disease severity in IgH[MOG] mice. In the CNS parenchyma and dura mater of IgH[MOG] mice, we observe an increased frequency of CD4+PD-1+CXCR5- T cells that share numerous characteristics with the recently described T peripheral helper (Tph) cell subset. Further, CNS-infiltrating B and Tph cells from IgH[MOG] mice show increased reactive oxygen species (ROS) production. Meningeal inflammation, Tph-like cell accumulation in the CNS and B/Tph cell production of ROS were all reduced upon p19 blockade. Altogether, MOG-specific B cells promote autoimmune inflammation of the CNS parenchyma and meninges in an IL-23-dependent manner.